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    <br/>
    <br/>第二章 Java 数据分析算法引擎系统
    <br/> 作者: 罗瑶光, Author:Yaoguang.Luo<br/>
    <br/>
    <br/>基础应用: 元基催化与肽计算 编译机的仿生分析机
    <br/>


    <br/>非卷积视觉计算奠基溯源
    <br/>
    <br/>作者当是时萌生了一个想法, 如果卷积的内核算核越大, 那么每一个算子的循环遍历次数变增加,
    于是为了提升计算质量, 整体的速度和计算消费将指数提高, 举例, 100个矩阵数, 算核是2*2,
    那么需要遍历 100*2*2计算次数, 如果是3*3, 那么需要 100*3*3 计算次数, 4*4, 以此类推,
    则是计算 400次, 900次 , 1600次. 2500次, 损耗接近于m * n**2的指数翻倍. 作者思考, 是否
    有类似于快速卷积如O(n) 或者至少 是O(nlogn)的计算级别算法出现弥补这个缺陷. 这个需求极为迫切
    . 这个想法为后来的第十章的非卷积视觉计算奠基.
    <br/>
    <br/>描述人 罗瑶光
    <br/>
    <br/>3 ANN RNN DNN 线性深度卷积计算处理 refer page 222, 223, 223,
    <br/>
    <img class="banner_img" style="width: 100%" src="../images/5_7108/2/2_9.jpg"
         alt="浏阳德塔软件开发有限公司,罗瑶光"/>

    <br/>关于 Etl Unicorn对 音频卷积处理的文字描述
    <br/>
    <br/>作者在将计算机视觉的课程进行了pipe line etl处理后看了乐观的结果, 作者想,
    这些cnn卷积算法一定也可以处理线性的声音领域. 于是开始基于java sound包来分析语音, 如图所示
    wave read 的开头还有很多小点的噪音, 通过maxmin(低滤高滤)过滤后
    <br/>
    <br/>https://github.
    com/yaoguangluo/Data_Processor/blob/master/DP/soundProcessor/MaxMiniPro.
    java
    <br/>
    <br/>第71行 comp比例裁剪
    <br/>
    <br/>就没有噪点, 这张图片是作者从早期2014的测试截图, 当时还没有涉及傅里叶频率域变换,
    谐波噪声平滑等高级功能. 作者的当时主要动机是测试作者心跳
    <br/>
    <br/>About the prefix convolutional computing's, LWA (from left to the
    right, or from head to the tail), the author did a first conference at
    Shanghai Electrics, China, about the Butterworth low pass and Sawtooth
    wavelet. Then did a fulfillment of convolutional vision at Cal Lutheran,
    USA. The author considered not only the prefix convolutional computing's,
    the Arabic postfix RWA (from right to the left, or from tailto the
    head) also could be well in the wavelet.
    <br/>
    <br/>2019-06-23 Author's notes on Gitee. 2006, Nanjing, the author
    first touched the Fourier's formula set A*cosB+ B*sinA where in his
    bachelor's class of High Frequency Circut, HFC wavelet. At 2007, the
    author learned a derivation of AcosB and AsinB collections where in
    REVA institute. Bangalore University. And got LRC formula of Ui(t)=
    L*di(t)/dt+ 1/C*df i(t)*dt + R*i(t) with L*C*d*d*U0*(t)/dt*dt +
    R*C*d*U0(t)/dt + U0(t), Professor. JPSir, at Christ University, 2009.
    Then made a presentation of Fourier's transformation wavelet at KaiTong
    Ltd, Shanghai. After got a few experiance of frequent wavelt, the
    author continued a fulfillment of convolutional computer-vision at USA,
    and tested a FFT and DCT sound communications at Liuyang, 2014~2015.
    Finally tried to make an analyst of dyslexia-brains and partner with
    Sir. Newton Howard at Oxford University, UK.
    <br/>
    <br/>Once done of the Deta Parser at 2019, the author considered a
    VPCS could make a differential and integration in the same time. It was
    a symmetric statement. And the DNA's sessional encryption was the same
    too, for example the differential PDE -> PDS and an integration of PDS
    -> PDE. The author considered It could be used in parallel RNA IC
    disign.
    <br/>
    <br/>Implements of non convolutional vision-computing's. The author
    considered a convolutional matrix of kernel, was a centre of computing,
    means the scale size was 3, then will do 3* 3= 9 calculations by each
    set. If the scale size was 4, means to do 4 * 4=16 times. So, the
    formula was M sets * O(N*N). Therefore, the author considered a non
    convolutional algorithms was needed by todays. For example, of DNA
    non-convolutional vision-computing's.
    <br/>
    <br/>Implements of convolutional wavelet-component by ETL Unicorn.
    <br/>
    <br/>Once done of computer vision at 2012, the author considered It
    could work as an Etl pipe node, then build a Unicorn vision tools. Then
    integrated a Java sound API, the below pictures did a well show of the
    proofs. for example, a max-min node pipe to the proportion-taylor node,
    to perform a heartbeats-filter.
    <br/>
    <br/>The author YaoguangLuo 稍后优化语法.
    <br/>
    <img class="banner_img" style="width: 100%" src="../images/5_7108/2/2_10.jpg"
         alt="浏阳德塔软件开发有限公司,罗瑶光"/>

    <br/>是否正常. (图中巴特沃斯是带通数值滤波
    <br/>
    <br/>https://github.
    com/yaoguangluo/Data_Processor/blob/master/DP/soundProcessor/ButterworthPro.java
    <br/>
    <br/>第69行 与拉普拉斯滤波
    <br/>
    <br/>https://github.
    com/yaoguangluo/Data_Processor/blob/master/DP/soundProcessor/LaplacianPro.java
    <br/>
    <br/>第70行 非一种模式) 描述人 罗瑶光
    <br/>
    <br/>非线性
    <br/>
    <br/>1 德塔的数据分析包 包含图论的非线性广度建模 refer page 226, 230.
    <br/>
    <br/>2 德塔的数据分析包 包含图论的非线性深度建模 refer page 230, 232.
    <br/>
    <br/>3 德塔的数据分析包 包含图论的非线性树建模 refer page 236, 243, 253.
    <br/>
    <br/>维度
    <br/>
    <br/>1 德塔的数据分析包 包含1维 语音数组计算实例 refer page 见智能声诊
    <br/>
    <br/>2 德塔的数据分析包 包含2维 图片卷积计算实例 refer page 见智能相诊
    <br/>
    <br/>3 德塔的数据分析包 包含3维 数据循环阶计算实例 refer page 见噪音识别, 三阶傅里叶应用,
    animation等
    <br/>
    <img class="banner_img" style="width: 100%" src="../images/5_7108/2/2_11.jpg"
         alt="浏阳德塔软件开发有限公司,罗瑶光"/>
    <br/>德塔三阶傅里叶计算定义: 一般指将线性的时序语音波进行傅里叶变换, 此时的波为 频率域波,
    通过简单的噪声频率过滤后,
    让后再进行第二次傅里叶变换. 于是输出的时序波结果会非常的均匀和格式化, 产生优美的平滑间隔峰区间,
    于是将此时序波第三次傅里叶变换,
    再次得到的频率波输出具有明确的间隔峰区间生物特征标记. 用于德塔语音识别.
    <br/>
    <br/>定义人 罗瑶光
    <br/>
    <br/>Deta three times FFT/DFT of higher-order, means a wavelet which
    makes a transformation from the timer sequence waves to frequency wave,
    a then makes a noise frequency filter first. At this time, does again a
    wavelet which makes a transformation where from frequency wave to timer
    sequence wave. An observation of sequence wave is more harmoniously and
    uniformly. Also, can do more valuable filters here, therefore, finally
    does the third wavelet which makes a transformation where from the
    timer sequence wave to frequency wave again, the output can be a raw
    data collection of voice recognizing and Bio-target.
    <br/>
    <br/>Author YaoguangLuo 稍后优化语法
    <br/>
    <br/>具体描述: 智能声诊断视频描述
    <br/>
    <br/>关于声音噪声处理的描述: 作者通过java sound的jdk开源语音包api 进行声卡录音处理,
    通过麦克风物理设备来进行
    data readline 函数 record 自然界声音, 这时候声音是一串串的1维 double 和 float
    (具体看api的版本配置)线性数据, 于是作者将这些线性的 数据进行每 1024 个数据循环进行一小段小段的提取进行
    <br/>
    <br/>第一次离散傅里叶DFT变换(快速傅里叶FFT也可以, 只是在处理偶数和对称数列比较好),
    这时候第一次 时序波变频率域波的
    波形变出来了, 于是作者进行更进截取低频的主要区间频率, 依次进行了拉伸, 和高斯平滑处理,
    然后把处理过的频率波进行波峰线提取,
    然后将波峰线进行极值化, 更进向左平移对齐(方便格式化取值), 进行
    <br/>
    <br/>第二次离散傅里叶DFT变换, 于是一个稳定的平滑的时序波就出现了. 作者将这个稳定的时序波进行
    单个的间隔峰裁剪,
    因为只要一个震荡周期区间即可标识特定周期循环出现声音, (不需要多个 时序的震荡周期,
    当然多个可以统计增加精准),
    于是开始将这个裁剪拉伸的单个间隔峰时序波 进行
    <br/>
    <br/>第三次离散傅里叶DFT变换, 这时候频率就比较稳定了. 这个3次傅里叶声音计算过程,
    可以用来做声音标记等,
    广泛应用于声音类的工业场景. 作者的测试实例主要来自作者(罗瑶光)自己的AOEIU 元音发音记录实例.
    <br/>
    <br/>描述人 罗瑶光
    <br/>
    <br/>场景
    <br/>
    <br/>1 图片的操作. refer page 214
    <br/>
    <br/>2 像素的操作. refer page 见视觉
    <br/>
    <img class="banner_img" style="width: 100%" src="../images/5_7108/2/2_12.jpg"
         alt="浏阳德塔软件开发有限公司,罗瑶光"/>

    <br/>智能相诊多媒体图片描述
    <br/>
    <br/>作者给养疗经的智能相诊设计了视频暂停, 录入的功能, 首先, 视频录入方便了养疗经进行视频快放和倒放的功能,
    这样视频的多媒体处理能力得到了加强, 其次方便截图和截图的图片进行像素级别操作,
    如单一帧图片的像素画图和像素标记,
    标记的图片帧又能融入视频中, 复合加强视频多媒体处理能力. 描述人 罗瑶光
    <br/>
    <br/>3 文件的存储. refer page 214.
    <br/>
    <br/>4 语音的处理. refer page 见听觉.
    <br/>
    <br/>仿生听觉
    <br/>
    <br/>1 滤噪计算 高斯1D, median refer page 206, 213, 260.
    <br/>
    <br/>2 频率变换 傅里叶, 快速傅里叶 refer page 258
    <br/>
    <img class="banner_img" style="width: 100%" src="../images/5_7108/2/2_13.jpg"
         alt="浏阳德塔软件开发有限公司,罗瑶光"/>


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